PubMed | Georgetown University, University of California at San Francisco, University of Illinois at Chicago, Cook County Health and Hospitals System and Memory and Aging CenterType: Journal Article | Journal: The Journal of infectious diseases | Year: 2016

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture.
For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain.
Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data.
"The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis.
"Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality.
A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development.
As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts.
"We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown.
To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds.
Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram.
"The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown.
But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views.
"Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition."
The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013.
"At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch."
After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health.
Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture.
For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain.
Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data.
"The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis.
"Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality.
A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development.
As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts.
"We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown.
To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds.
Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram.
"The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown.
But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views.
"Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition."
The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013.
"At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch."
After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health.
Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture.
For the first time, a new tool developed at the Department of Energy’s (DOE’s) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer’s spread throughout the brain.
Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data—like heat maps, node link diagrams and anatomical views—to provide context for brain connectivity data.
“The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before,” says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis.
“Other tools tend to look at abstract or statistical network connections but don’t do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context,” says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool’s functionality.
A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool’s development.
As a neuroscientist at UCSF’s Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer’s and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts.
“We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That’s the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections,” says Brown.
To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. “For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c,” Brown adds.
Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they’d also look at fMRI data that had been reduced to a static network diagram.
“The problem with this analysis process is that it’s all static. If I wanted to explore another region of the brain, which would be a different pattern, I’d have to input a whole different set of data and create another set of static images,” says Brown.
But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views.
“Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn’t realize existed before in the dataset. Every time I use it, I find something surprising in the data,” says Brown. “It is also incredibly valuable for researchers who don’t know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition.”
The idea for Brain Modulyzer initiated when Berkeley Lab’s Weber and Seeley met at the “Computational Challenges for Precision Medicine” in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries—a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013.
“At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools,” says Brown. “The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch.”
After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called “Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data.” Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health.
Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. “We want to make sure that this tool is useful to the community, so we will keep iterating on it,” says Brown. “We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time.”
In addition to Brown, Weber, Murugesan and Seeley other authors on the paper are Bernd Hamann (UC Davis), Andrew Trujillo (UCSF) and Kristopher Bouchard (Berkeley Lab).

Frontotemporal dementia (FTD), the second most common cause of dementia in people under 65, may be triggered by a defect in immune cells called microglia that causes them to consume the brain’s synaptic connections, according to new research led by UCSF scientists.
The new study – published April 21, 2016 in the journal Cell — adds to growing evidence that the brain’s immune system is a driving force behind many neurodegenerative diseases, and suggests new approaches to diagnosing and treating patients with FTD, which currently affects as many as 22 out of 100,000 adults, with typical onset between the ages of 45 and 65.
Microglia normally act as the brain’s garbage collectors, disposing of foreign particles such as viruses or bacteria as well as sick and dying brain cells. In the developing brain, microglia also help refine the brain’s circuitry by pruning back unneeded neural connections, which are marked for destruction with immune molecules called “complement proteins.” Recent studies by Harvard neurobiologist Beth Stevens, Ph.D., and others have suggested that this process may go awry during adolescence in patients with schizophrenia, and as a side effect of aging in patients with Alzheimer’s disease: in both cases an overabundance of complement protein appears to cause too many synapses to be tagged for destruction by the microglia.
“The brain’s innate immune system is emerging as a common pathway behind many neurodegenerative disorders,” said senior author Eric Huang, M.D., Ph.D., a professor of pathology at UCSF and pathologist at the UCSF-affiliated San Francisco VA Medical Center. “This idea has been controversial, however, because in human patients, neurodegeneration is typically accompanied by some degree of inflammation, with lots of activated microglia, but it’s hard to tell whether that is a driver of the degeneration or a consequence. You need careful experiments using animals models to dissect the cause-effect relationship.”
Working with colleagues at UCSF’s Memory and Aging Center and departments of Neurology and Neurological Surgery, as well as the UCSF-affiliated Gladstone Institutes, Stanford University, and others, Huang and his team compared brain tissue from human FTD patients with familial mutations in the progranulin gene to the brains of mice with this gene deleted. In the mice, the defect caused age-related neurodegeneration and excessive grooming akin to the obsessive-compulsive disorder (OCD) symptoms seen in human FTD patients.
The researchers found that as the mice aged, the mutation caused a gradual breakdown of microglial cells’ waste disposal systems, which led to excessive activation of these cells’ aggressive immune functions, heightened production of complement proteins, and excessive synaptic pruning in the thalamus, a part of the brain that is highly relevant to human FTD.
Additional experiments on isolated microglia made it clear to the researchers that progranulin normally acts as a brake to prevent excessive microglia activation. Without it, it appeared that an unknown aspect of the normal aging process allowed microglia to spiral out of control.
However, the researchers showed that they could short-circuit this death spiral by deleting the gene for one of the major complement proteins produced by microglia, called C1qa. Mice with both the progranulin and the C1qa genes turned off lived considerably longer than those with intact C1qa, and didn’t develop OCD-like behaviors. Their brains also showed a drastic reduction in the number of activated microglia and much better protection from synapse loss.
“We worked for more than two years to get to that result,” Huang said, “But when we did, it was a really surreal experience. It was immediately clear that blocking complement protein might be a good therapeutic target for FTD patients with progranulin mutations.”
Research points to new approaches for dementia patients
Huang and his team are now collaborating with a biotech company called Annexon to test therapies that block C1qa. However, these treatments are likely still a long way off. For one thing, treatments for neurodegeneration likely need to be taken early, before the brain damage is done, and there’s still no way to reliably detect the disease before it’s far too late.
That’s why the researchers also investigated whether the elevated complement protein levels that would be predicted by their study show up in patients’ cerebrospinal fluid (CSF) — the fluid collected by a spinal tap. If so, they could potentially serve as a biomarker to allow early detection of the disease.
Initially they were disappointed by the results: “We looked at CSF from normal and diseased brains side by side, and they looked about the same,” Huang said. “But then we tried separating the FTD patients based on how severe their dementia had been when the CSF samples were taken, and the result was really spectacular. Clearly, as patients’ mental status declined, the level of complement protein in their CSF increased.”
Huang said he hopes future research will allow physicians to use this signal to enable earlier diagnosis and to test the effectiveness of potential treatments. If doctors give an anti-complement drug and the levels of complement protein in the CSF go down, he said, that would be a good sign that the immediate danger to the brain has been relieved.